Inference of Gene Regulatory Networks from Time Series Expression Data: A Data Mining Approach

2006 ◽  
Author(s):  
Patrick H. Ma ◽  
Keith C. Chan
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Expression Data ◽  
Data Mining Approach ◽  
Series Expression ◽  
Gene Regulatory

AN EFFECTIVE DATA MINING TECHNIQUE FOR RECONSTRUCTING GENE REGULATORY NETWORKS FROM TIME SERIES EXPRESSION DATA

2007 ◽  
Vol 05 (03) ◽  
pp. 651-668 ◽  
Author(s):  
PATRICK C. H. MA ◽  
KEITH C. C. CHAN
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Expression Data ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Statistical Verification ◽  
Series Expression ◽  
Gene Regulatory

Recent development in DNA microarray technologies has made the reconstruction of gene regulatory networks (GRNs) feasible. To infer the overall structure of a GRN, there is a need to find out how the expression of each gene can be affected by the others. Many existing approaches to reconstructing GRNs are developed to generate hypotheses about the presence or absence of interactions between genes so that laboratory experiments can be performed afterwards for verification. Since, they are not intended to be used to predict if a gene in an unseen sample has any interactions with other genes, statistical verification of the reliability of the discovered interactions can be difficult. Furthermore, since the temporal ordering of the data is not taken into consideration, the directionality of regulation cannot be established using these existing techniques. To tackle these problems, we propose a data mining technique here. This technique makes use of a probabilistic inference approach to uncover interesting dependency relationships in noisy, high-dimensional time series expression data. It is not only able to determine if a gene is dependent on another but also whether or not it is activated or inhibited. In addition, it can predict how a gene would be affected by other genes even in unseen samples. For performance evaluation, the proposed technique has been tested with real expression data. Experimental results show that it can be very effective. The discovered dependency relationships can reveal gene regulatory relationships that could be used to infer the structures of GRNs.

scholarly journals Learning the structure of gene regulatory networks from time series gene expression data

BMC Genomics ◽  
2011 ◽  
Vol 12 (Suppl 5) ◽  
pp. S13 ◽  
Author(s):  
Haoni Li ◽  
Nan Wang ◽  
Ping Gong ◽  
Edward J Perkins ◽  
Chaoyang Zhang
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Gene Expression Data ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Expression Data ◽  
Time Series Gene Expression ◽  
Gene Regulatory
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